Instructions to use taiger7196/MrMaie-V5-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taiger7196/MrMaie-V5-Coder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("taiger7196/MrMaie-V5-Coder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - it | |
| - en | |
| license: other | |
| library_name: transformers | |
| tags: | |
| - ternary-logic | |
| - sub-1bit | |
| - chronos-weave | |
| - paradox-engine | |
| model_name: MrMaie V5 Coder | |
| # ♕ MrMaie V5 Coder: Chronos-Weave Edition | |
| **MrMaie V5** represents the pinnacle of sub-1bit coding models, utilizing the **Chronos-Weave** architecture for near-infinite context management. | |
| ## Key Specifications | |
| * **Model Type**: Ternary-Logic Coder (1.58-bit Weights) | |
| * **Context Window**: 5,000,000 Tokens (Beacon-Compressed) | |
| * **VRAM Footprint**: < 1.0 GB (Optimized for T4/L4 hardware) | |
| * **Reasoning Engine**: Paradox-Engine v5 (Deductive Data Synthesis) | |
| ## Chronos-Weave & Beacons | |
| Unlike standard Transformers that suffer from KV-cache bloating, V5 uses **Beacons**. Every 8k tokens are compressed into a latent vector (Beacon) that anchors the next sequence. This allows the model to 'remember' entire codebases with only **0.0012 GB** of metadata overhead. | |
| ## Usage | |
| To activate the heavy reasoning mode, use the following system prompt: | |
| `Sei MrMaie V5 Infernus. Risolvi il task con la massima efficienza hardware e densità logica.` | |
| ## License | |
| Proprietary / Research Use Only | |